Text analysis of documents is a growing area of importance for organizations having a large number of documents. Organizations can utilize text analysis in an effort to derive valuable information from large pools of unstructured content. Natural Language Processing (NLP) is becoming an important aspect of text and document analysis, allowing valuable information to be gathered from documents written by authors of various demographics. A demographic of an author (i.e. location, age group, gender, etc.) can provide important information corresponding to a writing style of the author that can be utilized in text and document analysis. In text analysis, annotator algorithms are used to extract information from a document and provide annotations corresponding to text in the document. Documents can be processed by multiple different annotator algorithms utilized to analyze different types of data (i.e. annotator algorithms corresponding to different author demographics). Annotator algorithms correspond to certain word lists and rules that can be demographic specific (i.e. corresponding to speech patterns of authors form different demographics).